1. Field of the Invention
The present disclosure relates to network monitoring in communication networks, and more particularly, to determining Key Performance Indicators (KPIs) using inferential statistics.
2. Description of the Related Art
One typical need for Network Operators is measuring network performances, troubleshooting network nodes, interfaces, and the like, and controlling the services' behavior.
There are several use cases regarding these aspects as the deployment of new network technology in field, the control of new services or simply the performance management of the network to prevent instability, overload and other issues on the network.
In order to deal with these challenges, Network Operators often use external monitoring systems. These monitoring systems are typically connected to the network in non-intrusive mode, sniffing and processing the traffic data and providing feedbacks to the Network Operators. This output can be protocol analysis data, call trace data, service trends, aggregated measurements, and the like.
Key Performance Indicators (KPIs) are an example of aggregated measurements suitable for network monitoring. In brief, KPIs are performance, quality or traffic measurements done on the network, aggregated per time interval (e.g. every 5 min, etc.) and are typically tied up to “dimensions”, which are the objects they refer to. In order to provide effective KPIs for analyzing the behavior of Telecommunication networks, it is often necessary to account for information from different sources or network interfaces and correlate such information together.
For example, accounting for the information from different sources or network interfaces can include checking a specific service scenario spread all over the network (e.g. an end-to-end correlation of a mobile originating call with a mobile terminating call, etc.) or evaluating network behavior on specific trigger events that involves several elements in the network (e.g. an handover triggered on the source LTE radio access technology and completed on the target UTRAN radio access technology, etc.).
Accounting for the information and correlating such information is often the only method to gain information about a specific network scenario that involves complex relations on different interfaces or even on different radio access technologies.
However, correlating information and data is generally complex and also expensive in terms of monitoring resources. Despite efforts to date, such correlation remains a preferred technique for accounting for the information from various interfaces and sources. Accordingly, the approaches and techniques described herein provide for improved network monitoring to determine KPIs without requiring the expensive and complex traditional information correlation, but instead employ one or more inferential statistical techniques.